System and method for assessing cancer risk

ABSTRACT

Methods and systems for determining a probabilistic assessment of a person developing cancer are disclosed. The probabilistic assessment may include receiving a digital breast image of a person, selecting a region of interest within the received breast image, and analyzing this selected region of interest with respect to texture analysis. A probabilistic assessment may then be determined through the use of a logistic regression model based on the texture analysis within the region of interest and personal risk factors. A probabilistic assessment may also be determined through the use of a linear regression model based on the texture analysis within the region of interest and a known cancer indicator or risk factor.

RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent ApplicationSer. No. 61/074,321, filed Jun. 20, 2008, titled System and Method forAssessing Cancer Risk, which is incorporated fully herein by reference.

FIELD OF THE INVENTION

The present invention relates to assessing a person's risk fordeveloping cancer. Specifically, texture features of a person's imagealong with risk factors of the person are utilized to determine aprobabilistic assessment for developing cancer.

BACKGROUND OF THE INVENTION

Screening digital mammography (DM) and digital breast tomosynthesis(DBT) are tools for identifying latent breast cancers within apopulation, leading to improved outcomes of reduced mortality. For womenat increased risk of breast cancer, however, magnetic resonance imaging(MRI) although more expensive than DM and DBT, may provide superiorcapabilities in identifying early stage cancers, thereby justifyingtheir increased cost. As a result, cost reimbursement has beenauthorized for using MRI to screen women within high risk categories.There is an ever present desire to reduce medical cost by utilizing lessexpensive medical procedures, while maintaining a high quality of care.The present invention addresses this need among others.

SUMMARY OF THE INVENTION

In accordance with one aspect of the invention, methods and systems aredisclosed for assessing the risk of developing cancer. The risk ofdeveloping cancer may be assessed by receiving an image of a person,analyzing the image to obtain values representing characteristics of theimage, obtaining risk factors associated with the person, determining aprobabilistic assessment of the person developing cancer based on theobtained values and the obtained risk factors, and storing theprobabilistic assessment.

In accordance with another aspect of the invention, methods aredisclosed for selecting a region of interest (ROI) within a breastimage. The ROI may be selected by receiving a breast image, comparingthe breast image with other breast images to establish anatomiccorrespondences, and mapping a region identifier onto the breast imageto select the ROI based on the anatomic correspondences.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention is best understood from the following detailed descriptionin connection with the accompanying drawings, with like elements havingthe same reference numerals. According to common practice, the variousfeatures of the drawings are not drawn to scale. On the contrary, thedimensions of the various features are arbitrarily expanded or reducedfor clarity. The letter N may be used to refer to a non-specific numberof elements. Included in the drawings are the following figures:

FIG. 1 is a block diagram illustrating a computer architecture forassessing breast risk in accordance with an aspect of the presentinvention;

FIG. 2 is a flow diagram illustrating steps for assessing cancer risk inaccordance with an aspect of the present invention;

FIG. 3 a is an image depicting a prior art digital mammography (DM)system;

FIG. 3 b is an image depicting a prior art digital breast tomosynthesis(DBT) system;

FIG. 4 is a flow diagram illustrating the selection of a ROI within amimage in accordance with an aspect of the present invention;

FIG. 5 a is an image illustrating the selection of a ROI within a DMimage in accordance with an aspect of the present invention;

FIG. 5 b is an image illustrating the selection of a ROI within a DBTimage in accordance with an aspect of the present invention;

FIG. 5 c is a mediolateral oblique (MLO) view image illustrating theautomatic selection of a ROI within an image in accordance with anaspect of the present invention;

FIG. 5 d is a craniocaudal (CC) view image illustrating the automaticselection of a ROI within an image in accordance with an aspect of thepresent invention;

FIG. 6 is a flow diagram illustrating the analysis of an image by bitquantization and texture features in accordance with an aspect of thepresent invention;

FIG. 7 is a flow diagram illustrating the development and execution of alogistic regression model based on texture features and risk factors andthe development and execution of a linear regression model based ontexture features and known cancer indicators for assessing cancer riskin accordance with aspects of the present invention;

FIG. 8 is a flow diagram illustrating the development and execution of alinear regression model to estimate signal to noise ratio (SNR) based ontexture features for assessing image quality in accordance with anaspect of the present invention;

FIG. 9 is a table of the linear regression of texture features versusSNR in accordance with an aspect of the present invention;

FIG. 10 is a table of the linear regression of texture features andacquisition parameters versus SNR in accordance with an aspect of thepresent invention;

FIG. 11 is a table of the linear regression of texture features versusdose in accordance with an aspect of the present invention;

FIG. 12 is a table of the linear regression of texture features andacquisition parameters versus dose in accordance with an aspect of thepresent invention; and

FIG. 13 is a table of the linear regression of texture features versusbreast density in accordance with an aspect of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

Breast cancer risk may be assessed based solely on Gail risk factors,which are described below. A limitation of this type of assessment modelis its reliance of population statistics to identify at-risk persons. Asa result, it does not accurately predict a person's specific lifetimerisk of developing cancer. Other techniques focus on utilizing tissuecharacteristics within an image such as mammographic breast densityassessment to identify person-specific markers of breast cancer riskeither as a substitute or as an adjunct to the Gail risk tool.Nevertheless, these techniques are not certain, mostly do to thesubjective nature of the density assessment. Additionally, studiessuggest a relationship between mammographic parenchymal tissue and therisk of developing breast cancer. Parenchymal texture patterns in X-raybreast images are formed by the fatty, glandular, and stromal breasttissues.

FIG. 1 is a block diagram illustrating one embodiment of a computersystem 100 for assessing the risk of a person developing cancer. Thecomputer system 100 includes a user input device 102, an image inputdevice 104, a display device 106, a memory 108 and a processor 110. Inone example, user input device 102 may be a computer mouse and keyboardthat enables information to be manually input into system 100. In oneexample, image input device 104 may be an X-ray device that providesX-ray images such as DM and/or DBT images to system 100. Images may alsoinclude other digital images such as MRI, ultrasound, CT, optical, etc.In another example, image input device 104 may be another computer or amemory device for providing images to system 100. In one example,display 106 may be a computer monitor that visually displaysinformation. Suitable user input devices, image input devices (includingmedical acquisition systems and picture archiving and communicationsystems (PACs)), displays, memories, and processors for use inaccordance with the present invention will be understood by one of skillin the art from the description herein.

In an embodiment of the present invention, and as will be described infurther detail below, the illustrated computer system 100 operates inthe following manner: X-ray images of breast tissue are input throughimage input device 104. Person information and other parametersassociated with the assessment of cancer risk are input through userinput device 102. The output of user input device 102 and image inputdevice 104 are stored in memory 108 and processed by processor 110.Processor 110 processes the input information and the image information,assesses the risk of cancer, stores the results in memory 108, anddisplays the cancer risk assessment results on display 106.

FIG. 2 depicts a flow diagram 200 of exemplary steps for assessingbreast cancer risk in accordance with an aspect of the presentinvention. Briefly, in step 202 an image is captured, in step 204 theimage is received, in optional step 218 the image quality is evaluated,in optional step 206 a ROI within the received image is selected, instep 208 the image is analyzed, in step 210 risk factors of a person aredetermined, step 212 the probabilistic assessment of the persondeveloping cancer is calculated based on the analysis of the image andthe determined risk factors, in step 214 the assessment is stored inmemory, and in step 216 the assessment of the person developing canceris displayed.

The steps of flow diagram 200 will now be described in detail. In step202, an image is captured, e.g., by or via image input device 104. In anexemplary embodiment, a breast image is captured. In one embodiment, animage of a breast is captured using, for example, DM (FIG. 3 a) or DBT(FIG. 3 b). Other types of images may include digital imaging modalitiessuch as MRI, ultrasound, CT, optical imaging, etc. In the DM imagingsystem 300 depicted in FIG. 3 a, two-dimensional (2D) images areproduced by a compressed projection of the three-dimensional (3D) breastvolume. As shown in FIG. 3 a, breast 310 is inserted between compressionplate 308 and detector 312. X-rays 306 are then transmitted in thedirection of the breast 310 via X-ray tube 304. As X-rays 306 passthrough breast 310, they are detected by detector 312 to produce abreast image 504 as shown in FIG. 5 a. A consideration in the use of DMis that the images produced reflect the properties of the parenchymalbreast tissue and the properties of other tissue (e.g., skin andsubcutaneous fat layers), which may make it more difficult to assessbreast cancer. In general, skin and subcutaneous fat could be considerednoise in terms of image-based risk breast cancer characterization, thusincreasing the likelihood of erroneous results.

Similar to DM, the DBT system 302 depicted in FIG. 3 b also compressesbreast 310 between compression plate 308 and detector 312. In the DBTsystem, however, X-ray tube 304, which transmits x-rays 306, is rotatedat different angles with respect to the compressed breast 310. As X-raytube 304 is rotating at different angles with respect to breast 310, a3D image represented by 508(1)-508(N) of the breast tissue is producedas shown in FIG. 5 b. By combining the information from the different 2Dprojections, a 3D image is produced wherein the adjacent anatomicalbreast structures are filtered out, thus alleviating the effect oftissue superimposition. Thus, DBT imaging may offer a more accuratetexture analysis of the breast tissue than DM. Suitable DM and DBTdevices for use with the present invention will be understood by one ofskill in the art from the description herein.

Referring back to FIG. 2, in step 204, the captured image is received.In an exemplary embodiment, the captured image is received by processor110 (FIG. 1) from image input device 104 (FIG. 1) and stored in memory108 (FIG. 1). For example, breast images may be received from anothercomputer and stored in a database with memory 108. In another example,the breast images may be received directly from DM or DBT X-ray devices.In general, the images may be received from any electronic devicecapable of transmitting an image. Furthermore, the images may becompressed images or uncompressed images. The images may also bereceived over various mediums such as wired and/or wireless mediums.

In optional step 206, a ROI within the received image is selected. FIG.4 depicts a flow diagram 400 of steps for selecting a ROI within areceived image. Selection of the ROI is useful in assessing breastcancer risk, because the texture within the ROI is a significantcontributor in the risk assessment. Analysis of an incorrect orsuboptimal selected ROI may result in an erroneous assessment of breastcancer risk. It will be understood by one of skill in the art from thedescription herein, that the entire image may represent the ROI, inwhich case the selection of the ROI may be omitted because the entireimage is analyzed.

Selection of the ROI within the breast image may be done eitherautomatically (step 402) or manually (step 404) as illustrated in FIG.4. Manually selecting the ROI within the breast image as illustrated instep 404 is generally performed by a trained professional. Specifically,measurement lines are manually drawn on the image by inputtingcoordinates via a computer mouse or keyboard to identify relativedistances referenced to anatomic features of the breast such as thebreast nipple. Manual selection of the ROI is subjective to the user whois selecting the region. Thus, different users working with the samebreast image may select different regions of interest, which may lead tosuboptimal selection due to human error, for example.

Automatic selection 402 of the ROI is an alternative technique which mayyield more uniform selection results than manual selection 404. In oneembodiment, during automatic selection of the ROI image is compared toother images in a database in order to develop anatomic correspondences.The anatomic correspondences may be established through nipplesegmentation, statistical correlations such as mutual information,texture-based distance metrics, or other relevant similarity metrics.The resulting anatomic correspondences may be used to objectively mapthe ROI onto the images. For example, the registration correspondencemay yield a coordinate transformation that is used to objectively mapthe canonical retroareolar ROI onto subject images. The coordinatetransformation may be constrained to be topology preserving, therebyensuring the geometric integrity of the transformed ROI used to samplethe images. If necessary, multiple breast templates can be created tobetter reflect the distinct categories of breast anatomy that may berepresented, and these templates can be determined automatically bycluster analysis of the images. The automatic selection process may alsobe tailored to different breast sizes and different breast morphology tobetter serve a wide variety of persons.

A suitable algorithm for automatic selection of a ROI may be developedusing a fixed-breast population from a subset of one hundred imagesbased on an average, representative breast size. The automated routinecan be cored against the manual process to evaluate its efficacy anddetermine the robustness of the texture descriptors with respect to theROI placement. The algorithm can then be optimized to operate ondifferent breast sizes and different breast morphology for large-scaledata analysis.

In one embodiment, as shown in FIG. 5 c, the automatic selection of theROI is performed for a mediolateral oblique (MLO) image of a breast 550.Once the image is loaded, the edge of the pectoral muscle isautomatically located (by edge detection) and indicated by line 558. Thesystem then automatically locates nipple 556 as being the furthest edgeof breast 552 perpendicular from line 558. A perpendicular line 562 isthen drawn from 558 to nipple 556. The ROI 560 is then placed in thecenter of line 562.

In another embodiment, as shown in FIG. 5 d, the automatic selection ofthe ROI is performed for a craniocaudal (CC) image of a breast 570. Oncethe image is loaded, the system automatically locates nipple 556 asbeing the furthest edge of breast 552 from the side of the image. Aperpendicular line 562 is then drawn from the side of the image tonipple 556. The ROI 560 is then placed in the center of line 562.

In an exemplary embodiment, once the ROI is automatically placed, theROI may then be manually adjusted by a trained professional, forexample. The manual adjustment may fine tune the automatic ROI placementfor obtaining a more optimal area of the breast for analysis.Furthermore, the trained professional may also manually draw pectoralline 558 in the MLO image and manually select nipple 556 in either theMLO or CC image.

FIG. 5 a illustrates X-ray images 504 using a DM imaging technique 500and FIG. 5 b illustrates a 3D X-ray image represented by 508(1)-508(N)using a DBT imaging technique 502 of breasts wherein a ROI has beenselected. Specifically, breast image 504, as captured by the DM imagingtechnique 500, illustrates a single ROI 506 represented by a squareregion identifier 505 mapped onto the DM image 504. In contrast, when aROI is selected from the 3D breast image by the DBT imaging technique502, two techniques may be employed. In accordance with a firsttechnique, a ROI is selected within each of multiple 2D imageprojections that reconstruct a 3D image. In accordance with a secondtechnique, a ROI is selected directly from a 3D image represented by(510(1)-510(N)) reconstructed from the multiple 2D image projections. Ingeneral, a region identifier 509 is either mapped onto each 2D image,thereby producing multiple 2D regions of interest, or it is mapped ontothe 3D image, thereby producing one 3D ROI.

Referring back to FIG. 2, in step 208 the image is analyzed, e.g., byprocessor 110. An embodiment for analysis of the image is now describedwith reference to the steps illustrated in flow diagram 600 (FIG. 6). Instep 602, the pixel values within the image are quantized to a reducebit range (step 602). Quantization may be performed using one of the twoquantization algorithms described below. Other suitable algorithms willbe understood by one skilled in the art from the description herein. Ina first quantization algorithm, each pixel value in the ROI is quantizedto the same number of bits. For example, each pixel regardless of itsbit range will be quantized to one of (16, 32, 64, 128, 256, 512, 1024,2048 . . . etc.) bits. In a second quantization algorithm, the pixelswithin the ROI are quantized to the same degree. Quantizing the bitranges to the same degree may be accomplished by cutting the bit range(e.g., by ¼, ⅛, 1/16. . . etc.). Quantizing the bit ranges to the samedegree, may provide better accuracy for computing texture features thanto the same number of bits because scaling by a certain degree does notdiscriminate against any particular bit range (e.g., large or small bitranges).

In step 604, analysis of the quantized image is performed by computingone or more texture features. In general, the texture features may becomputed in either 2D or 3D depending on the dimensions of the selectedimage. One example of a texture feature is skewness of a histogram ofthe image. For example, when the image is predominantly composed of fat,the histogram skewness tends to be positive, whereas when the texture isprimarily composed of dense tissue, the histogram skewness tends to benegative. Skewness of a histogram is known to be the third statisticalmoment and may be computed in accordance with equation 1.

$\begin{matrix}{{{skewness} = \frac{w_{3}}{w_{2}^{3/2}}},{w_{k} = {\sum\limits_{i = 0}^{g_{\max}}\;{{n_{i}( {i - \overset{\_}{i}} )}^{k}/N}}},{N = {\sum\limits_{i = 0}^{g_{\max}}\; n_{i}}},{\overset{\_}{i} = {\sum\limits_{i = 0}^{g_{\max}}\;( {i\;{n_{i}/N}} )}}} & (1)\end{matrix}$In equation 1, n_(i) represents the number of times that gray levelvalue i occurs in the image region, and g_(max) is the maximumgray-level value, and N is the total number of image pixels. Bycomputing the skewness of the ROI, the system is able to assess thedensity of the breast tissue.

Another example of a texture feature is coarseness. Small coarsenessvalues indicate fine texture (high detail), whereas high coarsenessvalue indicates coarse texture (low detail).

In one embodiment, coarseness is computed in accordance with equation 2based on a Neighborhood Gray Tone Difference Matrix (NGTDM).

$\begin{matrix}{{{coarseness} = {( {\sum\limits_{i = 0}^{g_{\max}}\;{p_{i}{v(i)}}} )^{- 1}\mspace{14mu}{and}}}{{v(i)} = \begin{Bmatrix}{{\sum\;{{i - {\overset{\_}{L}}_{i}}}}\mspace{11mu}} & {\;{{{for}\mspace{14mu} i}\; \in {{\{ n_{i} \}{\mspace{11mu}\;}{if}\mspace{14mu} n_{i}} \neq 0}}} \\0 & {otherwise}\end{Bmatrix}}} & (2)\end{matrix}$In equation 2, g_(max) is the maximum gray-level value, p_(i) is theprobability that gray level i occurs, {n_(i)} is the set of pixelshaving gray level value equal to i, and the inverse of L is calculatedin accordance with equation 3 for a 2D image, or equation 4 for a 3Dimage.

$\begin{matrix}{{\overset{\_}{L}}_{i} = {\frac{1}{S - 1}{\sum\limits_{k = {- t}}^{t}\;{\sum\limits_{l = {- t}}^{t}\;{j( {{x + k},{y + l}} )}}}}} & (3) \\{{\overset{\_}{L}}_{i} = {\frac{1}{S - 1}{\sum\limits_{k = {- t}}^{t}\;{\sum\limits_{l = {- t}}^{t}\;{\sum\limits_{q = {- t}}^{t}\;{j( {{x + k},{y + l},{z + q}} )}}}}}} & (4)\end{matrix}$

In 2D equation 3, j(x,y) is the pixel located at (x,y) with gray levelvalue i, (k,l)≠(0,0) and S=(2t+1)² with, for example, t=1, or othersuitable value, specifying the neighborhood size around the pixellocated at (x,y).

In 3D equation 4, j(x,y,z) is the voxel located at (x,y,z) with graylevel value i, (k,l,z)≠(0,0,0), and S=(2t+1)³ with, for example, t=1, orother suitable value, specifying the 3D voxel window around (x,y,z).

Other examples of texture features are contrast and energy. Computationsof contrast and energy for a 2D image are determined from a gray-levelco-occurrence matrix based on the frequency of the spatial co-occurrenceof gray-level intensities in the image. Computations of contrast andenergy for a 3D image are determined from a gray-level co-occurrencematrix for a displacement vector within the 3D image. Specifically, theprobability of occurrence of voxel pair of gray levels whose spatiallocations are a selected displacement vector apart, is used to computecontrast and energy in the 3D image. In this embodiment, contrastquantifies overall variation in image intensity, while energy is ameasure of image homogeneity. Contrast, energy and homogeneity may becomputed in accordance with equations 5, 5 and 7 for a 2D image.

$\begin{matrix}{{contrast} = {\sum\limits_{i = 0}^{g_{\max}}\;{\sum\limits_{j = 0}^{g_{\max}}\;{{{i - j}}^{2}{C( {i,j} )}}}}} & (5) \\{{energy} = {\sum\limits_{i = 0}^{g_{\max}}\;{\sum\limits_{j = 0}^{g_{\max}}{C( {i,j} )}}}} & (6) \\{{homogeneity} = {\sum\limits_{i = 0}^{g_{\max}}\;{\sum\limits_{j = 0}^{g_{\max}}\frac{C( {i,j} )}{1 + {{i - j}}}}}} & (7)\end{matrix}$In equations 5, 6 and 7, g_(max) is the maximum gray-level value and Cis the normalized co-occurrence matrix.

In a 3D image, to compute contrast, energy, and homogeneity, thegray-level co-occurrence statistics required for the computation of theco-occurrence matrix are estimated based on the spatial co-occurrencefrequencies of voxel gray-level values within the entire 3D ROI volume.In an exemplary embodiment, a 3D displacement vector d=(dx, dy, dz) isdefined around each voxel along the x, y, and z dimensions, wheredx=dy=dz=1 is the voxel offset; with 26 neighboring voxel-pairs in 13independent symmetric directions. Texture features in this embodimentmay be calculated in each of these 13 directions and then averaged tocreate a single measure. In alternative embodiments, more or fewer than13 directions may be utilized. Additionally, the calculated texturefeatures may be processed using other statistical techniques, e.g.,median, mode, minimum, or maximum.

Another example of a texture feature for a 2D and 3D image, is the ratiobetween pixel values located in concentric circles (2D) or spheres (3D)that are centered at a central point, and pixel values of the entireROI. The area (2D) and volume (3D) properties of the ROI obtainedthrough this method may be utilized along with the other texturefeatures in the logistic regression model for assessing cancer risk of aperson. The volume properties of the ROI are computed utilizingequations 8 and 9.

$\begin{matrix}{{f_{s}(i)} = \frac{N_{\theta}( {m,r_{i}} )}{N_{SPHERE}( {m,r_{i}} )}} & (8) \\{{f_{r}(i)} = \frac{N_{\theta}( {m,r_{i}} )}{N_{\theta}( {m,r_{k}} )}} & (9)\end{matrix}$

In equation 8, fs measures the fraction of the circle or sphere occupiedby the ROI (wherein N_(θ)(m, r_(i)) is the area or volume of the ROIintersected by the circle or sphere, and N_(SPHERE)(m, r_(i)) is thearea of the circle or the volume of the sphere). In equation 9, frmeasures the fraction of the ROI occupied by the circle or sphere(wherein N_(θ)(m, r_(i)) is the area or volume of the ROI intersected bythe circle or sphere, and N_(θ)(m, r_(k)) is the area of the entire ROIor the volume of the entire ROI). θ denotes a ROI constructed by a setof voxels V={v_(i), i=1, . . . , z}, wherein m is the center of mass ofθ. R is a sub-region of θ that extends over a radius r from the centerof mass m. For characterizing θ, we define the radial voxel countingfunction as:N _(θ)(m,r)=|{v _(i) εR}|, where ∀_(i) , v _(i) εR:|m−v _(i) |≦r  (10)The radial voxel counting function N_(θ)(m, r) counts the number ofvoxels v that belong to the sub-region R. In other words, this functionenumerates the voxels v that belong to the intersection of a sphere ofradius r (circle for 2D ROIs) and the ROI θ.

For the non-homogeneous regions, the contribution of each voxel isdetermined by its density content. Hence, the alternative radial voxeldensity counting function for the non-homogeneous ROIs is defined as:

$\begin{matrix}{{{N_{\theta}( {m,r} )} = {\sum\limits_{i}\;{{density}{\mspace{11mu}\;}{content}\mspace{14mu}( v_{i} )}}},{{where}\mspace{14mu}{\forall_{i,}{v_{i} \in {R:{{{m - v_{i}}} \leq r}}}}}} & (11)\end{matrix}$This alternative density counting function calculates the sum of thedensity content of each voxel v_(i) that belongs to the sub-region Rdefined by the intersection of a sphere of radius r (circle for 2D ROIs)and the ROI θ.

Referring back to FIG. 2, in step 210, risk factors of the person aredetermined. Risk factors of a person require additional pieces ofinformation that may improve the accuracy of assessing a person's breastcancer risk. In one embodiment, the risk factors that are used forbreast cancer assessment are Gail risk factors. Specifically, the Gailrisk factors may include unique information for each person such as, forexample, current age of the person, age when the person startedmenstruating, previous breast biopsies of the person, age of the personat first birth, and person's family history of breast cancer infirst-degree relatives. These risk factors are determined independent ofanalyzing the breast image.

In step 212, a probabilistic assessment of the person developing canceris determined. FIG. 7 depicts a flow chart 700 of two exemplaryprocesses for use in determining a probabilistic assessment of cancerrisk. One process for assessing cancer risk is the execution of logisticregression (steps 702-704) with the texture features and risk factors.Another process to assessing cancer risk is the execution of linearregression (steps 706-710) between the texture features and a knowncancer indicator such as breast density.

In step 702, a logistic regression model is developed. In one example,the model includes texture features computed by analyzing the breastimage produced in step 208 (e.g., histogram skew, coarseness, energy andcontrast) and person risk factors such as the Gail risk factors. Thelogistic regression model may be adjusted for factors such as menopause,body mass index (BMI), ethnicity, etc.

In step 704, a probabilistic assessment of developing breast cancer isdeveloped through execution of the logistic regression model. In oneexample, the model expresses the log-odds (natural log of theprobabilistic ratio of an event occurring versus the event notoccurring) for cancer as a combination of the texture features and thepotential risk factors. Determination of significant predictors mayemploy the log-likelihood ratio test, which is a statistical test formaking a decision between two hypotheses based on the value of thisratio. The maximum likelihood estimates for the logistic regressioncoefficient may be obtained for each separate risk factor and thecorresponding odds ratios are estimated with associated confidenceintervals. The log-odds ratio provided by the model is converted into acorresponding probability of developing cancer. Thus, the systemutilizes the information obtained from the texture analysis of the ROIof the breast image as well as the person-specific risk factors toperform a logistic regression thus producing a probabilistic assessmentof the person developing cancer.

Breast density has been shown to correlate with a persons risk ofobtaining breast cancer. Specifically, higher density correlates tohigher risk of obtaining breast cancer. By performing linear regressionon the extracted texture features of the image, the breast density ofthe person may be computed, and thus aid in the assessment of theperson's cancer risk.

In step 706, a linear regression model is developed. In one example, themodel includes texture features computed by analyzing the breast imageproduced in step 208 (e.g., histogram skewness, coarseness, energy andcontrast). Linear regression shows high correlation between varioustexture features and breast density in both DM and DBT images. In theDBT image, as breast density increases, texture features such ascoarseness, fractal dimension increase in value while features such ascontrast decrease in value. Exemplary linear regression values (e.g.,coefficients and statistics) of texture features versus breast densityare shown in the table of FIG. 13.

Previously, breast density has been determined manually orsemi-automatically based on the intensity values of the pixels withinthe image. This simplified determination may not be accurate becauseintensity values of the pixels may be high due to factors other thanbreast density. Additionally, manual, pixel-wise classification of animage to determine breast density, which is typically performed usinggray-level image thresholding, is subjective with high inter-observervariability. Also, these techniques are not fully automated and aredifficult to standardize. With the strong correlations between thetexture features and a known breast density, however, a more accuratedetermination of breast density may be attained. By increasing theaccuracy in computing breast density in step 708, the accuracy of thebreast cancer risk assessment in step 710 is also increased.

In this embodiment, breast density is utilized as the known cancerindicator correlated with the texture features. It is contemplated thatother known indicators could also be used in assessing breast cancerrisk. It is also contemplated that when dealing with other types ofcancer (e.g. brain, lung, oral etc.) that other known indicators whichare correlated with the texture features may be used to assess cancerrisk.

Image quality is important in making an accurate assessment of cancerrisk. In general, assessments made from images with high quality scoresmay be trusted more, whereas assessments made from images with low imagequality scores may be trusted less. The relationship between imageacquisition parameters of an imaging device and texture featuresestimate signal to noise ratio, which may be utilized to compute animage quality score. By performing linear regression, the relationshipbetween the image acquisition parameters of the imager and texturefeatures of the image may be modeled in order to access the imagequality score.

In various embodiments of the invention, cancer risk of a patient isassessed based on texture features within the image. The accuracy ofthese texture features may deteriorate due to poor image quality therebyreducing the reliability of the assessment. In general, image quality isdependent on noise contributed by the patient and the system. Forexample, system noise in an image may be due to sub-acquisitionparameters on the X-ray machine or deterioration of the X-ray machine.Patient noise in an image may be due to anatomical features such as skinand fat tissue, or may be due to sub-optimal positioning of the patientduring the X-ray. Additionally, image quality may be affected by thetechnician improperly performing the test, e.g., by setting up theequipment incorrectly or improperly positioning the patient on theequipment during the test.

To improve the reliability of the cancer risk assessment, it may bebeneficial to determine the quality of the image. In step 218 of FIG. 2,an evaluation is performed to determine the quality of the image. Step218 is shown in greater detail in FIG. 8 as steps 802 and 804.Alternatively, the quality of the image may be determined independentlyfrom assessing cancer risk. For example, the quality of the image may beused to determine if the imaging equipment needs to berepaired/replaced, or to support the diagnostic decision of theinterpreting radiologist.

FIG. 8 depicts a flow diagram illustrating the development and executionof a linear regression model to estimate signal to noise ratio (SNR)based on texture features for assessing image quality in accordance withan aspect of the present invention. At step 802, a linear regressionmodel is developed. The linear regression model may be developed bydetermining the relationship between texture features and signal tonoise ratio (SNR). At step 804, a linear regression is executed tocompute image quality.

In FIGS. 9 and 10, linear regression is shown to have a strongcorrelation between various texture features/acquisition parameters andsignal to noise ratio (SNR) of the image and, in FIGS. 11 and 12, linearregression is shown to have a strong correlation between various texturefeatures/acquisition parameters and dose of the image.

As SNR increases, texture features such as skewness, coarseness andfractal dimension increase in value while features such as contrast,energy and homogeneity decrease in value. Likewise, as dose increases,texture features such as skewness, coarseness and fractal dimensionincrease in value while features such as contrast, energy andhomogeneity decrease in value. Exemplary stepwise multiple linearregression values of texture features versus SNR are shown in the tableof FIG. 9 and versus dose are shown in the table of FIG. 11.

In another embodiment, when utilizing acquisition parameters such astarget/filter and kilo-volt (kV) as additional predictor variables inthe linear regression, a stronger correlation to SNR is observed. Also,when utilizing acquisition parameters such as target/filter andkilo-volt (kV) as additional predictor variables in the linearregression, a stronger correlation to dose is observed. Exemplarystepwise multiple linear regression values of texture features andacquisition parameters versus SNR are shown in the table of FIG. 10 andversus dose are shown in the table of FIG. 12.

With the strong correlations shown in FIGS. 9, 10, 11, and 12, thequality of the image may therefore be accurately computed by the texturefeatures and/or acquisition parameters. In one example, once the imagequality is determined, a hard pass/fail decision may be output from theimage quality assessment step 218 to the cancer risk assessment step 212in FIG. 2. If the hard decision indicates a failure (computed SNR islow), the probabilistic assessment for that image may be discarded.After the failure, a new image may be taken and the test may beperformed again. If the image fails multiple times, the X-ray machinemay be taken offline (i.e. removed from service) for maintenance.

In another example, if a soft decision is output from the image qualityassessment step 218 to the cancer assessment step 212, the probabilisticassessment of cancer may be tagged with a soft reliability numberbetween 0% (minimum reliability) and 100% (maximum reliability). Forexample, if the soft decision indicates a poor SNR value, theprobabilistic assessment of cancer may be tagged with a poor reliabilitynumber such as 30%. The trained professional may then vary theacquisition parameters and obtain a new image to re-test the patient inan attempt to increase the reliability number to an adequate SNRthreshold. Providing feedback of the reliability number allows thetrained professional to determine the effects of varying acquisitionparameters on the image quality. There is a strong correlation betweentextures of a woman's left and right breast. This strong correlationindicates that certain texture features are inherent in a particularperson. Thus, the extracted texture features of the image may be used todetermine a unique identifier for each person (e.g. similar to afingerprint). By performing linear regression on the extracted texturefeatures of the image, a unique identifier for each person may beestablished. A possible use for the established unique identifier may befor identifying one person from another. Correctly identifying a personsimage is critical for minimizing insurance fraud (the same image cannotbe claimed by multiple persons), as well as malpractice (the imagecannot be mistaken for the wrong person).

In step 214 the assessment is stored in memory and in step 216 theassessment is displayed. In one example, the assessment may be stored byprocessor 110 (FIG. 1) in memory 108 for permanent storage. In anotherexample, the assessment may be temporarily stored in memory 108 (whichacts as a buffer) prior to display by display 106.

The various aspects of the present invention discussed above, providethe following advantages:

One advantage is a fully automated system for accurately accessingcancer risk of a person. The fully automated system removes the burdenof estimating risk from the technician/practitioner, and also minimizeshuman error. The automated assessment is useful in tailoring cancertreatments and forming preventive strategies, especially for women athigh risk. Thus, a more comprehensive and personalized assessment can beprovided utilizing techniques of the present invention.

Another advantage is the personal assessment of each person dependent onan automatically generated logistic regression model. The logisticregression model provides an assessment that is tailored to each personsunique features, thus more accurately assessing cancer on a personallevel.

Another advantage is the personal assessment of each person dependent onan automatically generated linear regression model. The linearregression model computes a known risk factor such as breast density toassess cancer.

Another advantage is determining a unique identifier for each person.The unique identifier ensures correct identification of each image, thuspreventing insurance fraud (same image cannot be used under differentnames), identity theft (unique identifier is not usable like a socialsecurity number) and malpractice (images cannot be mixed up).

Another advantage is determining image quality. Image quality affectstexture features of an image, and thus cancer assessment. Determiningimage quality allows the system to perform a more accurate assessment ofcancer risk.

One or more of the steps described above may be embodied in computerprogram instructions for execution by a computer. The computer programinstructions, when executed by a computer, cause the computer to performthese one or more steps. The computer program instructions may beembodied in computer readable media.

Although the invention illustrated and described herein with specificembodiments, the invention is not intended to be limited to the detailsshown, rather various modifications may be made and the details withinthe scope and range equivalents of the claims and without departing fromthe invention. For example, although the present invention has beendescribed for use in determining the risk of developing breast cancer,it is it is contemplated that the method can be used to assess the riskof developing other types of cancer, e.g., brain, lung, oral, etc.

What is claimed:
 1. A method for assessing risk of developing cancer,comprising: receiving an image of a person; analyzing the image todetermine texture values representing characteristics of the image;calculating a unique identifier for the person using the determinedtexture values, the texture values for calculating the unique identifierincluding skewness, coarseness, and contrast; obtaining risk factorsassociated with the person; determining a probabilistic assessment ofthe person developing cancer based on the determined texture values andthe obtained risk factors; and storing the probabilistic assessment. 2.The method of claim 1, wherein the received image is a breast image. 3.The method of claim 2, wherein the breast image is a digital mammography(DM) image.
 4. The method of claim 2, wherein the breast image is adigital breast tomosynthesis (DBT) image.
 5. The method of claim 1,wherein the analyzing step includes: selecting a region of interestwithin the image; and analyzing the image within the selected region ofinterest to obtain the values representing characteristics of the image.6. The method of claim 5, wherein the selecting step comprises: manuallyselecting the region of interest within the image.
 7. The method ofclaim 5, wherein the selecting step comprises: automatically selectingthe region of interest within the image by comparing the image to otherimages to establish anatomic correspondences, the anatomiccorrespondences established by segmentation, statistical correlationsand texture metrics.
 8. The method of claim 7, wherein the image is amediolateral oblique (MLO) view of a breast image and the automaticallyselecting includes: locating a pectoral muscle; locating a nipple;drawing a line perpendicular from the pectoral muscle to the nipple; andselecting the region of interest at a point along the perpendicularline.
 9. The method of claim 7, wherein the image is a craniocaudal (CC)view of a breast image and the automatically selecting includes:locating a side of the image; locating a nipple; drawing a lineperpendicular from the side of the image to the nipple; and selectingthe region of interest at a point along the perpendicular line.
 10. Themethod of claim 5, wherein the determined texture values representingthe characteristics of the image further include energy of the region ofinterest (ROI), ratios between the pixel values in the ROI and pixelvalues in a segmented portion of the ROI.
 11. The method of claim 5,wherein a computer is programmed to determine the probabilisticassessment by developing a logistic regression model based on at leastone image feature of the region of interest and the risk factorsassociated with the person and determining the probabilistic estimationof the person developing cancer based on the logistic regression model.12. The method of claim 11, wherein the at least one image feature is atexture value.
 13. The method of claim 5, wherein the selecting stepcomprises: comparing the image with other images to establish anatomiccorrespondences; and mapping a region identifier onto the image toselect the region of interest based on the anatomic correspondences. 14.The method of claim 5, wherein a computer is programmed to determine theprobabilistic assessment by developing a linear regression model basedon at least one image feature of the selected region of interest andbreast density and determining the probabilistic estimation of theperson developing cancer based on the linear regression model.
 15. Themethod of claim 14, wherein the at least one image feature is a texturevalue.
 16. The method of claim 1, further comprising: developing alinear regression model based on texture features of the region ofinterest and signal to noise ratio (SNR) of the image; and determiningimage quality based on the linear regression model.
 17. The method ofclaim 1, wherein a computer is programmed to determine the probabilisticassessment of the person developing cancer.
 18. A method for developinga logistic regression model for a person developing breast cancer,comprising: receiving an image of breast tissue for a person; selectinga region of interest within the image; analyzing the region of interestto determine texture values of the image; calculating a uniqueidentifier for the person using the determined texture values, whereinthe texture values for calculating the unique identifier includeskewness, coarseness, and contrast; developing a logistic regressionmodel, by a computer programmed to develop the logistic regression modelbased on the texture values in the region of interest and risk factorsassociated with the person for use in determining a probabilisticassessment; and storing the logistic regression model.
 19. The method ofclaim 18, wherein the logistic regression model is developed utilizingthe risk factors, the risk factors computed from person information. 20.The method of claim 19, wherein the risk factors include Gail factors.21. The method of claim 20, wherein the Gail factors comprise one ormore of: current age of the person, age when the person startedmenstruating, previous breast biopsies of the person, age of person atfirst birth, and persons family history of breast cancer in first-degreerelatives.
 22. The method of claim 18, wherein the texture valuesfurther include at least one of ratios between the pixel values in theregion of interest (ROI) and pixel values in a segmented portion of theROI or energy.
 23. A system for assessing risk of developing cancer,comprising: means for receiving an image of a person; means foranalyzing the image to determine texture values representingcharacteristics of the image; means for calculating a unique identifierfor the person using the determined texture values, the texture valuesfor calculating the unique identifier including skewness, coarseness,and contrast; means for obtaining risk factors associated with theperson; means for determining a probabilistic assessment of the persondeveloping cancer based on the determined texture values and theobtained risk factors; and means for storing the probabilisticassessment.